Why_this_webapp Bibliography

The AI Index 2022 Annual Report / 2205.03468 / ISBN:https://doi.org/10.48550/arXiv.2205.03468 / Published by ArXiv / on (web) Publishing site

Report highlights
Chapter 1 Reseach and Development
Chapter 2 Technical Performance
Chapter 3 Technical AI Ethics
Chapter 4 The Economy and Education
Chapter 5 AI Policy and Governance
Appendix

Exciting, Useful, Worrying, Futuristic: Public Perception of Artificial Intelligence in 8 Countries / 2001.00081 / ISBN:https://doi.org/10.48550/arXiv.2001.00081 / Published by ArXiv / on (web) Publishing site

Abstract
CCS Concept
1 Introduction
2 Background
3 Methodology
4 Findings
5 Discussion
6 Conclusions
Acknowledgments
References
A Questionnaire - Selected Questions

Ethics of AI: A Systematic Literature Review of Principles and Challenges / 2109.07906 / ISBN:https://doi.org/10.48550/arXiv.2109.07906 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Research Method
4 Reporting the review
5 Detail results and analysis
6 Threats to validity
7 Conclusions and future directions
References
9 Appendices

AI Ethics Issues in Real World: Evidence from AI Incident Database / 2206.07635 / ISBN:https://doi.org/10.48550/arXiv.2206.07635 / Published by ArXiv / on (web) Publishing site

Abstract
1Introduction
2 Related Work
3 Method
4 Results
5 Discussion
6 Conclusion
References

The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis / 2206.03225 / ISBN:https://doi.org/10.48550/arXiv.2206.03225 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Study Methodology
4 Evaluation of Ethical AI Principles
5 Evaluation of Ethical Principle Implementations
6 Gap Mitigation
7 Threats to Validiity
8 Conclusion
Acknowledgment
References

A Framework for Ethical AI at the United Nations / 2104.12547 / ISBN:https://doi.org/10.48550/arXiv.2104.12547 / Published by ArXiv / on (web) Publishing site

Executive summary
Introductionn
1. Problems with AI
2. Defining ethical AI
3. Implementing ethical AI
Conclusion

Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance / 2206.11922 / ISBN:https://doi.org/10.48550/arXiv.2206.11922 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methodology
4 Results
5 Discussion
6 Conclusion
Author Contributions

Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society / 2001.04335 / ISBN:https://doi.org/10.48550/arXiv.2001.04335 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Near-Long
3 The Problem with the Near/Long-Term Distinction
4 A Clearer Account of Research Priorities and Disagreements
Recommendations and Conclusion
References

ESR: Ethics and Society Review of Artificial Intelligence Research / 2106.11521 / ISBN:https://doi.org/10.48550/arXiv.2106.11521 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 The ESR Process
4 Deployment and Evaluation
5 Discussion
6 Conclusion
7 Acknowledgments
A Appendix: Interview Protocol
References

On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services / 2111.01306 / ISBN:https://doi.org/10.48550/arXiv.2111.01306 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Need forEthical AI in Finance
3 Practical Challengesof Ethical AI
4 Conclusions & Outlook
References

A primer on AI ethics via arXiv- focus 2020-2023 / Kaggle / on (web) Publishing site

Section 1: Introduction and concept
Section 2: History and prospective
Section 3: Current trends 2020-2023
Section 4: Considerations and conclusions
Appendix A: Bibliographical references
Appendix B: Data and charts from arXiv

What does it mean to be a responsible AI practitioner: An ontology of roles and skills / 2205.03946 / ISBN:https://doi.org/10.48550/arXiv.2205.03946 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Methodology
4 Proposed competency framework for responsible AI practitioners
5 Discussion
6 Conclusion
Acknowledgments
References
Appendix A supplementary material

GPT detectors are biased against non-native English writers / 2304.02819 / ISBN:https://doi.org/10.48550/arXiv.2304.02819 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Results
Discussion
Acknowledgements
References
Materials and Methods

Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects / 2304.08275 / ISBN:https://doi.org/10.48550/arXiv.2304.08275 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Underlying Aspects
III. Interactions between Aspects
IV. Concluding Remarks
References

QB4AIRA: A Question Bank for AI Risk Assessment / 2305.09300 / ISBN:https://doi.org/10.48550/arXiv.2305.09300 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Question Bank: QB4AIRA
3 Evaluation
4 Conclusion
References

A multilevel framework for AI governance / 2307.03198 / ISBN:https://doi.org/10.48550/arXiv.2307.03198 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introductioon
2. A Multilevel Approach to AI Governance for Trust-Enhancing Practices
3. International and National Governance
4. Corporate Self-Governance
5. AI Literacy and Governance by Citizen
6. Psychology of Trust
7. Propensity to Trust
8. Ethics and Trust Lenses in the Multilevel Framework
9. Virtue Ethics
10. Conclusion
References

From OECD to India: Exploring cross-cultural differences in perceived trust, responsibility and reliance of AI and human experts / 2307.15452 / ISBN:https://doi.org/10.48550/arXiv.2307.15452 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Method
3. Results
4. Discussion and conclusion
Acknowledgments
References

The Ethics of AI Value Chains: An Approach for Integrating and Expanding AI Ethics Research, Practice, and Governance / 2307.16787 / ISBN:https://doi.org/10.48550/arXiv.2307.16787 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Theory
3. Methodology
4. Ethical Implications of AI Value Chains
5. Future Directions for Research, Practice, & Policy
6. Conclusion

Perceptions of the Fourth Industrial Revolution and Artificial Intelligence Impact on Society / 2308.02030 / ISBN:https://doi.org/10.48550/arXiv.2308.02030 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Literature Review
Methods
Results
Conclusion
References

Regulating AI manipulation: Applying Insights from behavioral economics and psychology to enhance the practicality of the EU AI Act / 2308.02041 / ISBN:https://doi.org/10.48550/arXiv.2308.02041 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Clarifying Terminologies of Article-5: Insights from Behavioral Economics and Psychology
3 Enhancing Protection for the General Public and Vulnerable Groups
4 Conclusion
References

From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence / 2308.02448 / ISBN:https://doi.org/10.48550/arXiv.2308.02448 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
What is Generative Artificial Intelligence?
Applications in Military Versus Healthcare
Identifying Ethical Concerns and Risks
GREAT PLEA Ethical Principles for Generative AI in Healthcare
Conclusion
Acknowledgements
Author Information
References

Ethical Considerations and Policy Implications for Large Language Models: Guiding Responsible Development and Deployment / 2308.02678 / ISBN:https://doi.org/10.48550/arXiv.2308.02678 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
System-role
Perturbation
Image-related
Hallucination
Generation-related
Bias and Discrimination of Training Data
Conclusion
References

Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI / 2308.04448 / ISBN:https://doi.org/10.48550/arXiv.2308.04448 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Policy scope
4 Centralized regulation in the US context
5 Crowdsourced safety mechanism
6 The dual governance framework
7 Limitations
8 Conclusion

Normative Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions / 2208.12616 / ISBN:https://doi.org/10.48550/arXiv.2208.12616 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Methodology
3 Taxonomy of ethical principles
4 Previous operationalisation of ethical principles
5 Gaps in operationalising ethical principles
6 Conclusion
References
A Methodology

Bad, mad, and cooked: Moral responsibility for civilian harms in human-AI military teams / 2211.06326 / ISBN:https://doi.org/10.48550/arXiv.2211.06326 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Responsibility in War
Computers, Autonomy and Accountability
Moral Injury
Human Factors
AI Workplace Health and Safety Framework
Discussion
Conclusion
References

The Future of ChatGPT-enabled Labor Market: A Preliminary Study / 2304.09823 / ISBN:https://doi.org/10.48550/arXiv.2304.09823 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Results
3 Discussion
4 Limitations
5 Methods
References

A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation / 2305.11391 / ISBN:https://doi.org/10.48550/arXiv.2305.11391 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Large Language Models
3 Vulnerabilities, Attack, and Limitations
4 General Verification Framework
5 Falsification and Evaluation
6 Verification
7 Runtime Monitor
8 Regulations and Ethical Use
9 Discussions
10 Conclusions
Acknowledgments
Reference

Getting pwn'd by AI: Penetration Testing with Large Language Models / 2308.00121 / ISBN:https://doi.org/10.48550/arXiv.2308.00121 / Published by ArXiv / on (web) Publishing site

Abstract
Keywords
1 Introduction
2 Background
3 LLM-based penetration testing
4 Discussion
5 A vision of AI-augmented pen-testing
6 Final ethical considerations
References

Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust / 2308.09979 / ISBN:https://doi.org/10.48550/arXiv.2308.09979 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Results
3 Discussion
4 Conclusions
5 Supplementary material
References

Targeted Data Augmentation for bias mitigation / 2308.11386 / ISBN:https://doi.org/10.48550/arXiv.2308.11386 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related works
3 Targeted data augmentation
4 Experiments
5 Conclusions
6 Acknowledgements
References

AIxArtist: A First-Person Tale of Interacting with Artificial Intelligence to Escape Creative Block / 2308.11424 / ISBN:https://doi.org/10.48550/arXiv.2308.11424 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Case study
Reflections
References

Exploring the Power of Creative AI Tools and Game-Based Methodologies for Interactive Web-Based Programming / 2308.11649 / ISBN:https://doi.org/10.48550/arXiv.2308.11649 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Advancements in AI and Web-Based Programming
3 Emergence of Creative AI Tools and Game-Based Methodologies
4 Enhancing User Experience through Creative AI Tools
5 Engaging Web-Based Programming with Game-Based Approaches
6 Unveiling the Potential: Benefits of Interactive Web-Based Programming
7 Navigating Constraints: Limitations of Creative AI and GameBased Techniques
8 Real-World Applications: Showcasing Innovative Implementations
9 Ethical Considerations in Integrating AI and Game Elements
10 Privacy Concerns in Interactive Web-Based Programming for Education
11 Bias Awareness: Navigating AI-Generated Content in Education
12 The Future Landscape: Creative AI Tools and Game-Based Methodologies in Education
13 Case Study Example: Learning Success with Creative AI and Game-Based Techniques
14 Conclusion & Discussion
References

Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection / 2308.12885 / ISBN:https://doi.org/10.48550/arXiv.2308.12885 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work on Data Excellence
3 Reliability and Reproducibility Metrics for Responsible Data Collection
4 Published Annotation Tasks and Datasets
5 Results
6 Discussion
7 Conclusions
References
A Agreement Analysis
B Variability Analysis
C Power analysis
D Stability analysis
E Replicability similarity analysis

Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph / 2308.13534 / ISBN:https://doi.org/10.48550/arXiv.2308.13534 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Methods and training process of LLMs
III. Comprehensive review of state-of-the-art LLMs
IV. Applied and technology implications for LLMs
V. Market analysis of LLMs and cross-industry use cases
VI. Solution architecture for privacy-aware and trustworthy conversational AI
VII. Discussions
VIII. Conclusion
References
Appendix A industry-wide LLM usecases

The Promise and Peril of Artificial Intelligence -- Violet Teaming Offers a Balanced Path Forward / 2308.14253 / ISBN:https://doi.org/10.48550/arXiv.2308.14253 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The evolution of artificial intelligence: from theory to general capabilities
3 Emerging dual-use risks and vulnerabilities in AI systems
4 Integrating red teaming, blue teaming, and ethics with violet teaming
5 Research directions in AI safety and violet teaming
6 A pathway for balanced AI innovation
7 Violet teaming to address dual-use risks of AI in biotechnology
8 Macrostrategy for responsible technology trajectories
9 The path forward
10 Supplemental & additional details
References

Artificial Intelligence in Career Counseling: A Test Case with ResumAI / 2308.14301 / ISBN:https://doi.org/10.48550/arXiv.2308.14301 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Literature review
3 Methods
4 Results and discussion
5 Conclusion
References

Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? / 2308.15399 / ISBN:https://doi.org/10.48550/arXiv.2308.15399 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 Related works
3 Theory and method
4 Experiment
5 Conclusion
References
Ethical statement
A Details of datasets
B Experimental details

The AI Revolution: Opportunities and Challenges for the Finance Sector / 2308.16538 / ISBN:https://doi.org/10.48550/arXiv.2308.16538 / Published by ArXiv / on (web) Publishing site

Table of contents and index
Executive summary
1 Introduction
2 Key AI technology in financial services
3 Benefits of AI use in the finance sector
4 Threaths & potential pitfalls
5 Challenges
6 Regulation of AI and regulating through AI
7 Recommendations
References

Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond / 2309.00064 / ISBN:https://doi.org/10.48550/arXiv.2309.00064 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Black box and lack of transparency
3 Bias and fairness
4 Human-centric AI
5 Ethical concerns and value alignment
6 Way forward
7 Conclusion
References

The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie / 2309.02029 / ISBN:https://doi.org/10.48550/arXiv.2309.02029 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related work
3. ChatGPT Training Process
4. Methods
5. Discussion
6. Conclusion
Funding
References

Pathway to Future Symbiotic Creativity / 2209.02388 / ISBN:https://doi.org/10.48550/arXiv.2209.02388 / Published by ArXiv / on (web) Publishing site

Contents
Introduction
Part 1 - 1 Generatives Systems: Mimicking Artifacts
Part 1 - 2 Appreciate Systems: Mimicking Styles
Part 1 - 3 Artistic Systems: Mimicking Inspiration
Part 2 Art Data and Human–Machine Interaction in Art Creation
Part 2 - 1 Biometric Signal Sensing Technologies and Emotion Data
Part 2 - 2 Motion Caputer Technologies and Motion Data
Part 2 - 3 Photogrammetry / Volumetric Capture
Part 2 - 4 Aesthetic Descriptor: Labelling Artefacts with Emotion
Part 2 - 5 Immersive Visualisation: Machine to Human Manifestations
Part 3 Towards a Machine Artist Model
Part 3 - 1 Challenges in Endowing Machines with Creative Abilities
Part 3 - 2 Machine Artist Models
Part 3 - 3 Comparison with Generative Models
Part 3 - 4 Demonstration of the Proposed Framework
Part 4 NFTs and the Future Art Economy
Part 5 Ethical AI and Machine Artist
Part 5 - 1 Authorship and Ownership of AI-generated Works of Artt
Part 5 - 2 Algorithmics Bias in Art Generation
Part 5 - 3 Democratization of Art with new Technologies
References
Acknowledgment

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging / 2109.09658 / ISBN:https://doi.org/10.48550/arXiv.2109.09658 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Fairness - For Equitable AI in Medical Imaging
3. Universality - For Standardised AI in Medical Imaging
4. Traceability - For Transparent and Dynamic AI in Medical Imaging
5. Usability - For Effective and Beneficial AI in Medical Imaging
6. Robustness - For Reliable AI in Medical Imaging
7. Explainability - For Enhanced Understanding of AI in Medical Imaging
8. FUTURE-AI Quality Check
9. Discussion and Conclusion
Acknowledgments
References

The Cambridge Law Corpus: A Corpus for Legal AI Research / 2309.12269 / ISBN:https://doi.org/10.48550/arXiv.2309.12269 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Cambridge Law Corpus
3 Legal and Ethical Considerations
4 Experiments
5 Conclusion
General References
Legal References
A Detailed Information on Corpus Content
B Example XML case
C Case Outcome Task Description
D Case Outcome Annotation Instructions
E Topic Model Top Words
F Evaluation of GPT Models
Cambridge Law Corpus: Datasheet

EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval / 2310.00970 / ISBN:https://doi.org/10.48550/arXiv.2310.00970 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Dataset Construction
4 Modeling Ethics
5 Experiments
6 Conclusions
Acknowledgments
Appendix
References

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities / 2310.08565 / ISBN:https://doi.org/10.48550/arXiv.2310.08565 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction and Motivation
II. AI-Robotics Systems Architecture
III. Survey Approach & Taxonomy
IV. Attack Surfaces
V. Ethical & Legal Concerns
VI. Human-Robot Interaction (HRI) Security Studies
VII. Future Research & Discussion
VIII. Conclusion
Acknowledgements
References

If our aim is to build morality into an artificial agent, how might we begin to go about doing so? / 2310.08295 / ISBN:https://doi.org/10.48550/arXiv.2310.08295 / Published by ArXiv / on (web) Publishing site

Abstract
1 The Top-Down Approach Alone Might Be Insufficient
2 Emotion, Sentience and Morality
3 Proposing a Hybrid Approach
4 AI Governance Principles
5 Towards Moral AI
6 Acknowledgments
References

Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks / 2310.07879 / ISBN:https://doi.org/10.48550/arXiv.2310.07879 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 Method
4 Taxonomy of AI Privacy Risks
5 Discussion
6 Conclusion
Acknowledgments
References

ClausewitzGPT Framework: A New Frontier in Theoretical Large Language Model Enhanced Information Operations / 2310.07099 / ISBN:https://doi.org/10.48550/arXiv.2310.07099 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Nation-State Advances in AI-driven Information Operations
Theoretical Impact of LLMs on Information Operations
ClausewitzGPT and Modern Strategy
Mathematical Foundations
Ethical and Strategic Considerations: AI Mediators in the Age of LLMs
Integrating Computational Social Science, Computational Ethics, Systems Engineering, and AI Ethics in LLMdriven Operations
Looking Forward: ClausewitzGPT
Conclusion
References

The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements / 2310.06269 / ISBN:https://doi.org/10.48550/arXiv.2310.06269 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Research Design and Methodology
3 Analysis and Findings
4 Discussion
5 Conclusion
Acknowledgements
References
A Consent and Data Collection Processes
B Pre-class Questionnaire (Verbatim)
C In-class Activity
D Post-Activity Questionnaire (Verbatim)
G Statistical Tests

A Review of the Ethics of Artificial Intelligence and its Applications in the United States / 2310.05751 / ISBN:https://doi.org/10.48550/arXiv.2310.05751 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Literature Review
3. AI Ethical Principles
4. Implementing the Practical Use of Ethical AI Applications
5. Conclusions and Recommendations
References
Authors

A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics / 2310.05694 / ISBN:https://doi.org/10.48550/arXiv.2310.05694 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. WHAT LLMS CAN DO FOR HEALTHCARE? FROM FUNDAMENTAL TASKS TO ADVANCED APPLICATIONS
III. FROM PLMS TO LLMS FOR HEALTHCARE
IV. TRAIN AND USE LLM FOR HEALTHCARE
V. EVALUATION METHOD
VI. IMPROVING FAIRNESS, ACCOUNTABILITY, TRANSPARENCY, AND ETHICS
VII. FUTURE WORK AND CONCLUSION
Acknowledgments
References

STREAM: Social data and knowledge collective intelligence platform for TRaining Ethical AI Models / 2310.05563 / ISBN:https://doi.org/10.48550/arXiv.2310.05563 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 STREAM: Social data and knowledge collective intelligence platform for TRaining Ethical AI Models
3 The applications of STREAM
4 Conclusion and Future Work
References
Acknowledgments
Author contributions statement
Competing interests

Regulation and NLP (RegNLP): Taming Large Language Models / 2310.05553 / ISBN:https://doi.org/10.48550/arXiv.2310.05553 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Regulation: A Short Introduction
3 LLMs: Risk and Uncertainty
4 Scientific Expertise, Social Media and Regulatory Capture
5 Regulation and NLP (RegNLP): A New Field
6 Conclusion
Limitations
References

Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industry / 2310.05414 / ISBN:https://doi.org/10.48550/arXiv.2310.05414 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Research Methodology
3. Ethics of AI and Robotics
4. Systematic Review and Scientometric Analysis
5. Ethical Issues of AI and Robotics in AEC Industry
6. Discussion
7. Future Research Direction
8. Conclusion
References

Commercialized Generative AI: A Critical Study of the Feasibility and Ethics of Generating Native Advertising Using Large Language Models in Conversational Web Search / 2310.04892 / ISBN:https://doi.org/10.48550/arXiv.2310.04892 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Background and Related Work
Pilot Study: Text SERPs with Ads
Evaluation of the Pilot Study
Ethics of GEnerating Native Ads
Conclusion
References

Compromise in Multilateral Negotiations and the Global Regulation of Artificial Intelligence / 2309.17158 / ISBN:https://doi.org/10.48550/arXiv.2309.17158 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. The practice of multilateral negotiation and the mechanisms of compromises
3. The liberal-sovereigntist multiplicity
4. Towards a compromise: drafting the normative hybridity
5. Text negotiations as normative testing
6. Conclusion
Notes
Bibliography
Annex 1. Text amendments and ambiguity

Towards A Unified Utilitarian Ethics Framework for Healthcare Artificial Intelligence / 2309.14617 / ISBN:https://doi.org/10.48550/arXiv.2309.14617 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Why Ethics
Utilitarian Ethics
Principal Ethics in Healthcare
Method
Results and Discussion
A Unified Utilitarian Ethics Framework
Theory and Practical Implications
Conclusion
References

Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework / 2309.14530 / ISBN:https://doi.org/10.48550/arXiv.2309.14530 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Methods for Comprehensive Review
3. Clinical Risks
4. Technical Risks
5. Conclusion
References
Appendix

Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward / 2309.14213 / ISBN:https://doi.org/10.48550/arXiv.2309.14213 / Published by ArXiv / on (web) Publishing site

Table of Contents
1. Introduction
2. Autonomous vehicles
3. Existing Models
4. Traffic Flow prediction in Autonomous vehicles
5. Cybersecurity Risks
6. Risk management
7. Issues
8. Conclusion
9. References

The Return on Investment in AI Ethics: A Holistic Framework / 2309.13057 / ISBN:https://doi.org/10.48550/arXiv.2309.13057 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. AI Ethics
3. Return on Investment (ROI)
4. A Holistic Framework
5. Discussion
6. References

An Evaluation of GPT-4 on the ETHICS Dataset / 2309.10492 / ISBN:https://doi.org/10.48550/arXiv.2309.10492 / Published by ArXiv / on (web) Publishing site

2 Datasets and Methods
3 Results
4 Discussion
References
A GPT-4’s Training Set Personality Profile

Who to Trust, How and Why: Untangling AI Ethics Principles, Trustworthiness and Trust / 2309.10318 / ISBN:https://doi.org/10.48550/arXiv.2309.10318 / Published by ArXiv / on (web) Publishing site

Introduction
Trust
Trust in AI
Different Types of Trust
Trust and AI Ethics Principles
Trust in AI as Socio-Technical Systems
Conclusion
Acknowledgements
References

In Consideration of Indigenous Data Sovereignty: Data Mining as a Colonial Practice / 2309.10215 / ISBN:https://doi.org/10.48550/arXiv.2309.10215 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Definitions of Terms
3 Objectives
4 Methodology
5 Relating Case Studies to Indigenous Data Sovereignty and CARE Principles
6 Discussion
7 Conclusions and Recommendations
References

The Glamorisation of Unpaid Labour: AI and its Influencers / 2308.02399 / ISBN:https://doi.org/10.48550/arXiv.2308.02399 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Harms of Influencer Marketing
3 Ethical Data Collection, Responsible AI Development, and the Path Forward
4 Conclusion
References

AI & Blockchain as sustainable teaching and learning tools to cope with the 4IR / 2305.01088 / ISBN:https://doi.org/10.48550/arXiv.2305.01088 / Published by ArXiv / on (web) Publishing site

Authors
Abstract
1. Introduction
2. AI and blockchain in education: An overview of the benefits and challenges
3. AI-powered personalized learning: Customized learning experiences for learners
4. Blockchain-based credentialing and certification
5. AI-powered assessment and evaluation
6. Blockchain-based decentralized learning networks
7. AI-powered content creation and curation
8. Case studies: AI and blockchain in education
9. Challenges of AI and Blockchain in Teaching and Learning
10.Conclusion
11.References

Toward an Ethics of AI Belief / 2304.14577 / ISBN:https://doi.org/10.48550/arXiv.2304.14577 / Published by ArXiv / on (web) Publishing site

1. Introduction
2. “Belief” in Humans and AI
3. Why We Need an Ethics of AI Belief
4. Proposed Novel Topics in an Ethics of AI Belief
5. Nascent Extant Work that Falls Within the Ethics of AI Belief
6. Conclusion
References

Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles / 2304.11530 / ISBN:https://doi.org/10.48550/arXiv.2304.11530 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Ethical concerns of AI in medicine
Ethical datasets and algorithm development guidelines
Towards solving key ethical challenges in Medical AI
Ethical guidelines for medical AI model deployment
Discussion
Conclusion and future directions
References

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering / 2209.04963 / ISBN:https://doi.org/10.48550/arXiv.2209.04963 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Methodology
3 Governance Patterns
4 Process Patterns
5 Product Patterns
6 Related Work
7 Threats to Validity
8 Conclusion
References

The Ethics of AI Value Chains: An Approach for Integrating and Expanding AI Ethics Research, Practice, and Governance / 2307.16787 / ISBN:https://doi.org/10.48550/arXiv.2307.16787 / Published by ArXiv / on (web) Publishing site

Bibliography
Appendix A: Integrated Inventory of Ethical Concerns, Value Chains Actors, Resourcing Activities, & Sampled Sources

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare / 2309.12325 / ISBN:https://doi.org/10.48550/arXiv.2309.12325 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Materials and Methods
3 FUTURE-AI Guideline
4 Discussion
References
Appendix A Tables
Appendix B Full Author Affiliations

Language Agents for Detecting Implicit Stereotypes in Text-to-Image Models at Scale / 2310.11778 / ISBN:https://doi.org/10.48550/arXiv.2310.11778 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Agent Design
3 Agent Benchmark
4 Agent Performance
5 Related Work
6 Conclusion and Future Work
References
Appendix A Data Details
Appendix B Experiment Details

Specific versus General Principles for Constitutional AI / 2310.13798 / ISBN:https://doi.org/10.48550/arXiv.2310.13798 / Published by ArXiv / on (web) Publishing site

Abstract
Contents
1 Introduction
2 AI feedback on specific problematic AI traits
3 Generalization from a Simple Good for Humanity Principle
4 Reinforcement Learning with Good-for-Humanity Preference Models
5 Related Work
6 Discussion
7 Contribution Statement
References
A Model Glossary
B Trait Preference Modeling
C General Prompts for GfH Preference Modeling
D Generalization to Other Traits
E Response Diversity and the Size of the Generating Model
F Scaling Trends for GfH PMs
G Over-Training on Good for Humanity
H Samples
I Responses on Prompts from PALMS, LaMDA, and InstructGPT

The Self 2.0: How AI-Enhanced Self-Clones Transform Self-Perception and Improve Presentation Skills / 2310.15112 / ISBN:https://doi.org/10.48550/arXiv.2310.15112 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Method
4 Findings
5 Discussion
6 Conclusion
7 Limitation and Future Research
Acknowledgments
References
Annexed tables

Systematic AI Approach for AGI: Addressing Alignment, Energy, and AGI Grand Challenges / 2310.15274 / ISBN:https://doi.org/10.48550/arXiv.2310.15274 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Trifecta of AI Challenges
3 Systematic AI Approach for AGI
4 Systematic AI for Energy Wall
5 System Design for AI Alignment
6 System Insights from the Brain
7 Conclusions
References

AI Alignment and Social Choice: Fundamental Limitations and Policy Implications / 2310.16048 / ISBN:https://doi.org/10.48550/arXiv.2310.16048 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Reinforcement Learning with Multiple Reinforcers
3 Arrow-Sen Impossibility Theorems for RLHF
4 Implications for AI Governance and Policy
5 Conclusion
References

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges / 2310.16360 / ISBN:https://doi.org/10.48550/arXiv.2310.16360 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Review Methodology
III. UAV Platform Type
IV. Artificial Intelligence Embedded UAV
V. Challenges and Future Aspect on AI Enabled UAV
VI. Review Summary
VII. Conclusion
References
Authors Bios

Unpacking the Ethical Value Alignment in Big Models / 2310.17551 / ISBN:https://doi.org/10.48550/arXiv.2310.17551 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Risks and Ethical Issues of Big Model
3 Investigating the Ethical Values of Large Language Models
4 Equilibrium Alignment: A Prospective Paradigm for Ethical Value Alignmen
5 Conclusion
References

Moral Responsibility for AI Systems / 2310.18040 / ISBN:https://doi.org/10.48550/arXiv.2310.18040 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Causal Models
3 The BvH and HK Definitions
4 The Causal Condition
5 The Epistemic Condition
6 Degree of Responsibility
7 Conclusion and Future Work
Acknowledgments and Disclosure of Funding
References
Appendix

AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge / 2310.18852 / ISBN:https://doi.org/10.48550/arXiv.2310.18852 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 A Formal Language of AI for Open Science
4 Optimizing an Openness Metric in AI for Science
5 Why Openness in AI for Science
6 Conclusion and Future Work
Acknowledgements
References

Artificial Intelligence Ethics Education in Cybersecurity: Challenges and Opportunities: a focus group report / 2311.00903 / ISBN:https://doi.org/10.48550/arXiv.2311.00903 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
AI Ethics in Cybersecurity
Focus Group Protocol and Recruitment
Educational Challenges of Teaching AI Ethics in Cybersecurity and Core Ethical Principles
Pedagogical / Curricular Concerns Now and in the Future
Technical Issues
Learning Challenges
AI tool-specific educational concerns
Broader educational preparedness for work in AI Cybersecurity
Communication skills in cybersecurity and ethics
Conclusion
References

Human Participants in AI Research: Ethics and Transparency in Practice / 2311.01254 / ISBN:https://doi.org/10.48550/arXiv.2311.01254 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Contextual Concerns: Why AI Research Needs its Own Guidelines
3 Ethical Principles for AI Research with Human Participants
4 Principles in Practice: Guidelines for AI Research with Human Participants
5 Discussion
Acknowledgments
References
A Evaluating Current Practices for Human-Participants Research
B Placing Research Ethics for Human Participants in Historical Context
C Defining the Scope of Research Participation in AI Research
D A Note on Terminology

LLMs grasp morality in concept / 2311.02294 / ISBN:https://doi.org/10.48550/arXiv.2311.02294 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 A General Theory of Meaning
3 The Meaning Model
4 The Moral Model
5 Conclusion
A Supplementary Material
References

Educating for AI Cybersecurity Work and Research: Ethics, Systems Thinking, and Communication Requirements / 2311.04326 / ISBN:https://doi.org/10.48550/arXiv.2311.04326 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Literature Review
Research questions
Conclusions
References

Towards Effective Paraphrasing for Information Disguise / 2311.05018 / ISBN:https://doi.org/10.1007/978-3-031-28238-6_22 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methodology
4 Evaluation
5 Conclusion
References

Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metrics / 2311.05227 / ISBN:https://doi.org/10.48550/arXiv.2311.05227 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Overview of Kantian Deontology
3 Measuring Fairness Metrics
4 Deontological AI Alignment
5 Aligning with Deontological Principles: Use Cases
6 Conclusion

Unlocking the Potential of ChatGPT A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing / 2304.02017 / ISBN:https://doi.org/10.48550/arXiv.2304.02017 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Overview of ChatGPT and its capabilities
3 Transformers and pre-trained language models
4 Applications of ChatGPT in real-world scenarios
5 Advantages of ChatGPT in natural language processing
6 Limitations and potential challenges
7 Ethical considerations when using ChatGPT
8 Prompt engineering and generation
9 Future directions for ChatGPT and natural language processing
10 Future directions for ChatGPT in vision domain
11 Conclusion
References

Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies / 2304.07683 / ISBN:https://doi.org/10.48550/arXiv.2304.07683 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Sources of bias in AI
III. Impacts of bias in AI
IV. Mitigation strategies for bias in AI
V. Fairness in AI
VI. Mitigation strategies for fairness in AI
VII. Conclusions
References

Towards ethical multimodal systems / 2304.13765 / ISBN:https://doi.org/10.48550/arXiv.2304.13765 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 Related Work
3 Crafting an Ethical Dataset
4 A Multimodal Ethics Classifier
5 Conclusions
References

A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting) / 2310.04438 / ISBN:https://doi.org/10.48550/arXiv.2310.04438 / Published by ArXiv / on (web) Publishing site

Abstract
II. Introduction
III. Prehistoric prompting: pre NN-era
IV. History of NLP between 2010 and 2015: the pre-attention mechanism era
VI. 2015: birth of the transformer
VII. The second wave in 2017: rise of RL
VIII. The third wave 2018: the rise of transformers
IX. 2019: THE YEAR OF CONTROL
X. 2020-2021: the rise of LLMS
XI. 2022-current: beyond language generation
XII. Conclusions
Acknowledgment
References

Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents / 2310.15065 / ISBN:https://doi.org/10.48550/arXiv.2310.15065 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related work
3 Method
4 Findings
5 Discussion
6 Conclusion
Acknowledgments
References

She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models / 2310.18333 / ISBN:https://doi.org/10.48550/arXiv.2310.18333 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Works
3 ReFLeCT: Robust, Fair, and Safe LLM Construction Test Suite
4 Empirical Evaluation and Outcomes
5 Conclusion
References

Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control / 2311.08943 / ISBN:https://doi.org/10.48550/arXiv.2311.08943 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Humans In, On, and Out-of-the-Loop
III. Safety
IV. Trust
V. Ethics
VI. Conclusion
Acknowledgments
References

How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities / 2311.09447 / ISBN:https://doi.org/10.48550/arXiv.2311.09447 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methodology
4 Experiments
5 Conclusion
Limitations
Ethical Considerations
References

Prudent Silence or Foolish Babble? Examining Large Language Models' Responses to the Unknown / 2311.09731 / ISBN:https://doi.org/10.48550/arXiv.2311.09731 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 UnknownBench: Evaluating LLMs on the Unknown
3 Experiments
4 Related Work
5 Conclusion
References
A Limitations
B Confidence Elicitation Method Comparison
C Additional Details of the Dataset Construction
D Additional Results and Figures
E Prompt Templates
F NEC Question Generation Template

Revolutionizing Customer Interactions: Insights and Challenges in Deploying ChatGPT and Generative Chatbots for FAQs / 2311.09976 / ISBN:https://doi.org/10.48550/arXiv.2311.09976 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Chatbots Background and Scope of Research
3. Chatbot approaches overview: Taxonomy of existing methods
4. ChatGPT
5. Applications
6. Open chanllenges
7. Future Research Directions
8. Conclusion
References

Practical Cybersecurity Ethics: Mapping CyBOK to Ethical Concerns / 2311.10165 / ISBN:https://doi.org/10.48550/arXiv.2311.10165 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Methodology
4 Findings
5 Discussion
6 Limitations
7 Conclusion
References
A Ethics of the cyber security profession: interview guide

First, Do No Harm: Algorithms, AI, and Digital Product Liability Managing Algorithmic Harms Though Liability Law and Market Incentives / 2311.10861 / ISBN:https://doi.org/10.48550/arXiv.2311.10861 / Published by ArXiv / on (web) Publishing site

Bloustein Local and the Center for Urban Policy Research
Executive Summary
Preface
Introduction
The Problem
Why Liability Law?
Harms, Risk, and Liability Practices
Mitigation Tools
Conclusion
Appendix A - What is an Algorithmic Harm? And a Bibliography
Appendix B – Common AI Harms as Described by EPIC10
Appendix C - List of General Harms Created by Digital Products Provided by Claude.AI
Appendix D - List of Organization Acronyms
Appendix E - A Sampling of References Addressing Liability and Digital Products
About the Author

Case Repositories: Towards Case-Based Reasoning for AI Alignment / 2311.10934 / ISBN:https://doi.org/10.48550/arXiv.2311.10934 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Proposed Process
3 Related Work and Discussion
4 Conclusion
References

Responsible AI Considerations in Text Summarization Research: A Review of Current Practices / 2311.11103 / ISBN:https://doi.org/10.48550/arXiv.2311.11103 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background & Related Work
3 Methods
4 Findings
5 Discussion and Recommendations
6 Conclusion
7 Limitations
8 Ethical Considerations
Acknowledgements
References
A Statistics on Paper Annotators
B Methodology

Assessing AI Impact Assessments: A Classroom Study / 2311.11193 / ISBN:https://doi.org/10.48550/arXiv.2311.11193 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Study Design
4 Findings
5 Discussion
Acknowledgments and Disclosure of Funding
References
A Overview of AIIA Instruments
B Study Materials
C Extended Results

GPT in Data Science: A Practical Exploration of Model Selection / 2311.11516 / ISBN:https://doi.org/10.48550/arXiv.2311.11516 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background
III. Approach: capturing and representing heuristics behind GPT's decision-making process
IV. Comparative results
V. Conclusion and future work
VI. Future work
References

Responsible AI Research Needs Impact Statements Too / 2311.11776 / ISBN:https://doi.org/10.48550/arXiv.2311.11776 / Published by ArXiv / on (web) Publishing site

Abstract
Requiring adverse impact statements for RAI research is long overdue
What are other research communities doing?
What do RAI venues do?
Suggestions for More Meaningful Engagement with the Impact of RAI Research
Concluding Reflections
Acknowledgements
References

Large Language Models in Education: Vision and Opportunities / 2311.13160 / ISBN:https://doi.org/10.48550/arXiv.2311.13160 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Education and LLMS
III. Key technologies for EDULLMS
IV. LLM-empowered education
V. Key points in LLMSEDU
VI. Challenges and future directions
VII. Conclusion
Acknowledgment
References

The Rise of Creative Machines: Exploring the Impact of Generative AI / 2311.13262 / ISBN:https://doi.org/10.48550/arXiv.2311.13262 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Extent and impact of generative AI
III. Insights from top generative AI companies
IV. Risks of generative AI
V. Additional thoughts
VI. Conclusion
References

Towards Auditing Large Language Models: Improving Text-based Stereotype Detection / 2311.14126 / ISBN:https://doi.org/10.48550/arXiv.2311.14126 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Works
3 Methodology
4 Results and Discussion
5 Conclusion and Future Work
References
6 Appendix

Ethical implications of ChatGPT in higher education: A scoping review / 2311.14378 / ISBN:https://doi.org/10.48550/arXiv.2311.14378 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Methodology
Findings
Discussion and conclusion
References

Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review / 2311.14381 / ISBN:https://doi.org/10.48550/arXiv.2311.14381 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Overview of societal biases in GAI models
Methodology
Findings
Discussion
Conclusion
References

RAISE -- Radiology AI Safety, an End-to-end lifecycle approach / 2311.14570 / ISBN:https://doi.org/10.48550/arXiv.2311.14570 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Pre-Deployment phase
3. Production deployment monitoring phase
4. Post-market surveillance phase
5. Conclusion
Funding/Disclosures
Bibliography

Ethics and Responsible AI Deployment / 2311.14705 / ISBN:https://doi.org/10.48550/arXiv.2311.14705 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction: The Role of Algorithms in Protecting Privacy
2. Case Study of the Bletchley Summit
3. Ethical considerations in AI decision-making
4. Addressing bias, transparency, and accountability
5. Ethical AI design principles and guidelines
6. The role of AI in decision-making: ethical implications and potential consequences
7. Establishing responsible AI governance and oversight
8. AI in sensitive domains: healthcare, finance, criminal justice, defence, and human resources
9. Discussion on engaging stakeholders: fostering dialogue and collaboration between developers, users, and affected communities.
10. Conclusion
11. References

From deepfake to deep useful: risks and opportunities through a systematic literature review / 2311.15809 / ISBN:https://doi.org/10.48550/arXiv.2311.15809 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Material and methods
3. Results
4. Discussion
5. Conclusion
References

Generative AI and US Intellectual Property Law / 2311.16023 / ISBN:https://doi.org/10.48550/arXiv.2311.16023 / Published by ArXiv / on (web) Publishing site

Abstract
I. Very slowly then all-at-once
II. US Patent law
III. US Copyright law
IV. Caveart emptor: no free ride for automation
V. Potential harms and mitigation
VI. Conclusion
VII. Future considerations
References

Survey on AI Ethics: A Socio-technical Perspective / 2311.17228 / ISBN:https://doi.org/10.48550/arXiv.2311.17228 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Privacy and data protection
3 Transparency and explainability
4 Fairness and equity
5 Responsiblity, accountability, and regulations
6 Environmental impact
7 Conclusion
Acknowledgments
References

Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models / 2311.17394 / ISBN:https://doi.org/10.48550/arXiv.2311.17394 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background
III. The rise of large AI models
IV. Societal implications
V. Technical defense mechanisms
VI. Cross-platform strategies
VII. Ethical considerations
VIII. Proposed integrated defense framework
IX. Discussion
X. Conclusion
Acknowledgement
References

Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI / 2311.18252 / ISBN:https://doi.org/10.48550/arXiv.2311.18252 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Legal Basis of Privacy and Copyright Concerns over Generative AI
3 Mapping Challenges throughout the Data Lifecycle
4 Lifecycle Approaches
5 Conclusion
References

Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart Video Surveillance in Community Space / 2312.02078 / ISBN:https://doi.org/10.48550/arXiv.2312.02078 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related works
III. System features
IV. System setup and configuration
V. Real-world results and evaluation
VI. Physical-cyber-physical evaluation (anomaly detection)
VII. Conclusion
References
Biography

Understanding Teacher Perspectives and Experiences after Deployment of AI Literacy Curriculum in Middle-school Classrooms / 2312.04839 / ISBN:https://doi.org/10.48550/arXiv.2312.04839 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Methodology
3 Results
4 Conclusions
Acknowledgements
References

Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines / 2312.05235 / ISBN:https://doi.org/10.48550/arXiv.2312.05235 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
3. Literature review
4. Method
5. Results
6. Discussion
7. Conclusion

Contra generative AI detection in higher education assessments / 2312.05241 / ISBN:https://doi.org/10.48550/arXiv.2312.05241 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. The pitfalls in detecting generative AI output
3. Detectors are not useful
4. Teach critical usage of AI
5. Conclusion
Acknowledgements
References

Intelligence Primer / 2008.07324 / ISBN:https://doi.org/10.48550/arXiv.2008.07324 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Human intelligence
3 Reasoning
4 Bias, prejudice, and individuality
5 System design of intelligence
6 Measuring intelligence
7 Mathematically modeling intelligence
8 Consciousness
9 Augmenting human intelligence
10 Exceeding human intelligence
11 Control of intelligence
12 Large language models and Generative AI
13 Legal implications
14 Wrong numbers
15 Final thoughts
Acknowledgments
OPEN DASKALOS PROJECT SERIES
References

RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems / 2306.01774 / ISBN:https://doi.org/10.48550/arXiv.2306.01774 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related work
3 Methodology
4 Results & analysis
5 Discussion
6 Conclusion & future work
References

Ethical Considerations Towards Protestware / 2306.10019 / ISBN:https://doi.org/10.48550/arXiv.2306.10019 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background
III. Ethics: a primer
IV. Guidelines for promoting ethical responsibility
V. Implications whit future directions
References

Control Risk for Potential Misuse of Artificial Intelligence in Science / 2312.06632 / ISBN:https://doi.org/10.48550/arXiv.2312.06632 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Risks of Misuse for Artificial Intelligence in Science
3 Control the Risks of AI Models in Science
4 Call for Responsible AI for Science
5 Discussion
6 Related Works
References
7 Ethical Impacts
8 Acknowledgements
9 Author information
Appendix A Assessing the Risks of AI Misuse in Scientific Research
Appendix B Details of Risks Demonstration in Chemical Science
Appendix C Detailed Implementation of SciGuard
Appendix D Details of Benchmark Results

Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates / 2312.06861 / ISBN:https://doi.org/10.48550/arXiv.2312.06861 / Published by ArXiv / on (web) Publishing site

Abstract
...
Data Collection
Study 1: Geo-cultural Differences in Offensiveness
Study 2: Moral Foundations of Offensiveness
Study 3: Implications for Responsible AI
General Discussion
Geo-Cultural Factors
Moral Factors
References
A Appendix

Navigating the generative AI era: Introducing the AI assessment scale for ethical GenAI assessment / 2312.07086 / ISBN:https://doi.org/10.48550/arXiv.2312.07086 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Literature
Problematizing The View Of GenAI Content As Academic Misconduct
The AI Assessment Scale
Limitations and Future Research
Conclusion
References

Culturally Responsive Artificial Intelligence -- Problems, Challenges and Solutions / 2312.08467 / ISBN:https://doi.org/10.48550/arXiv.2312.08467 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
The concept of multiculturalism and its importance
Artificial intelligence – concept and ethical background
Culturally responsive AI – current landscape
Recommendations
Conclusion
References

Investigating Responsible AI for Scientific Research: An Empirical Study / 2312.09561 / ISBN:https://doi.org/10.48550/arXiv.2312.09561 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background and motivation
III. Research methodology
IV. Results
V. Discussion
VI. Conclusion and future work
References
Appendix A – Survey Questionnaire
Appendix B – Interview Questionnaire
Author’s Biographies

Designing Guiding Principles for NLP for Healthcare: A Case Study of Maternal Health / 2312.11803 / ISBN:https://doi.org/10.48550/arXiv.2312.11803 / Published by ArXiv / on (web) Publishing site

Abstract
1 Objective
2 Background and significance
3 Materials and methods
4 Results
5 Discussion
6 Conclusion
7 Acknowledgements
References
A Extended Survey Results
B Extended Guiding Principles
C Full survey questions

Beyond Fairness: Alternative Moral Dimensions for Assessing Algorithms and Designing Systems / 2312.12559 / ISBN:https://doi.org/10.48550/arXiv.2312.12559 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Reign of Algorithmic Fairness
3 Taking a Step Forward
4 Limitations
5 Conclusion
References

Learning Human-like Representations to Enable Learning Human Values / 2312.14106 / ISBN:https://doi.org/10.48550/arXiv.2312.14106 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related Work
3. Experiments on Synthetic Data
4. Experiments on Human Data using Language Models
5. Discussion
Acknowledgements
References
A. Appendix

The Economics of Human Oversight: How Norms and Incentives Affect Costs and Performance of AI Workers / 2312.14565 / ISBN:https://doi.org/10.48550/arXiv.2312.14565 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Theoretical background and hypotheses
III. Method
IV. Results
V. Discussion
VI. Conclusion
Appendix
References

Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning / 2312.17479 / ISBN:https://doi.org/10.48550/arXiv.2312.17479 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Experimental Study
Results
Discussion
Methods
Acknowledgments
References
Supplementary Material

Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence / 2401.00286 / ISBN:https://doi.org/10.48550/arXiv.2401.00286 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Foundations of AI-driven threat intelligence
3. Autonomous threat hunting: conceptual framework
4. State-of-the-art AI techniques in autonomous threat hunting
5. Challenges in autonomous threat hunting
6. Case studies and applications
7. Evaluation metrics and performance benchmarks
8. Future directions and emerging trends
9. Conclusion
References

Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review / 2401.01519 / ISBN:https://doi.org/10.48550/arXiv.2401.01519 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. LLMs in cognitive and behavioral psychology
3. LLMs in clinical and counseling psychology
4. LLMs in educational and developmental psychology
5. LLMs in social and cultural psychology
6. LLMs as research tools in psychology
7. Challenges and future directions
8. Conclusion
Acknowledgments

Synthetic Data in AI: Challenges, Applications, and Ethical Implications / 2401.01629 / ISBN:https://doi.org/10.48550/arXiv.2401.01629 / Published by ArXiv / on (web) Publishing site

1. Introduction
2. The generation of synthetic data
3. The usage of synthetic data
4. Risks and Challenges in Utilizing Synthetic Datasets for AI
5. Conclusions
References

MULTI-CASE: A Transformer-based Ethics-aware Multimodal Investigative Intelligence Framework / 2401.01955 / ISBN:https://doi.org/10.48550/arXiv.2401.01955 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related work
III. Methodology: model development
IV. System design
V. Evaluation
VI. Discussion and future work
VII. Conclusion
Acnkowledgments
References

AI Ethics Principles in Practice: Perspectives of Designers and Developers / 2112.07467 / ISBN:https://doi.org/10.48550/arXiv.2112.07467 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related work
III. Methods
IV. Results
V. Discussion and suggestions
VI. Support mechanisms
VII. Conclusion
Acknowledgements
References

Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models / 2310.19917 / ISBN:https://doi.org/10.48550/arXiv.2310.19917 / Published by ArXiv / on (web) Publishing site

Abstract
Background and significance
Objective
Materials and methods
Results
Discussion
Conclusion
Funding
Contributors
PRISMA 2020 flow diagram and bias handling workflow
References

Resolving Ethics Trade-offs in Implementing Responsible AI / 2401.08103 / ISBN:https://doi.org/10.48550/arXiv.2401.08103 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Approaches for Resolving Trade-offs
III. Discussion and Recommendations
IV. Concluding Remarks
References

Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making / 2401.08691 / ISBN:https://doi.org/10.48550/arXiv.2401.08691 / Published by ArXiv / on (web) Publishing site

Abstract
Contents / List of figures / List of tables / Acronyms
1 Introduction
I Understanding bias - 2 Bias and moral framework in AI-based decision making
3 Bias on demand: a framework for generating synthetic data with bias
4 Fairness metrics landscape in machine learning
II Mitigating bias - 5 Fairness mitigation
6 FFTree: a flexible tree to mitigate multiple fairness criteria
III Accounting for bias - 7 Addressing fairness in the banking sector
8 Fairview: an evaluative AI support for addressing fairness
9 Towards fairness through time
IV Conclusions
Bibliography

Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems / 2401.09473 / ISBN:https://doi.org/10.48550/arXiv.2401.09473 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Method
4 Results
5 Discussion
6 Conclusion
References

FAIR Enough How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training? / 2401.11033 / ISBN:https://doi.org/10.48550/arXiv.2401.11033 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 FAIR Data Principles: Theoretical Background and Significance
3 Data Management Challenges in Large Language Models
4 Framework for FAIR Data Principles Integration in LLM Development
5 Discussion
6 Conclusion
References
Appendices

Enabling Global Image Data Sharing in the Life Sciences / 2401.13023 / ISBN:https://doi.org/10.48550/arXiv.2401.13023 / Published by ArXiv / on (web) Publishing site

Abstract
1. Motivation for White Paper
2. Background
3. Use cases representing different image data types and their challenges and status for sharing
4. Towards global image data sharing
Towards Global Image Data Sharing: A to-do list for various stakeholders
References
International Working Group Members who contributed to the discussion and writing of the white paper (in alphabetical order)
Acknowledgements

Five ethical principles for generative AI in scientific research / 2401.15284 / ISBN:https://doi.org/10.48550/arXiv.2401.15284 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Principle 1: Understand model training and output
Principle 2: Respect privacy, confidentiality, and copyright
Principle 3: Avoid plagiarism and policy violations
Principle 4: Apply AI beneficially
Principle 5: Use AI transparently and reproducibly
Concluding remarks
References

A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations / 2401.17486 / ISBN:https://doi.org/10.48550/arXiv.2401.17486 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related work
3 Methods
4 RAI tool evaluation practices
5 Towards evaluation of RAI tool effectiveness
6 Limitations
7 Conclusion
References
A List of RAI tools, with their primary publication
B RAI tools listed by target stage of AI development
C List of publications, with their associated RAI tools
D Summary of themes and codes

Detecting Multimedia Generated by Large AI Models: A Survey / 2402.00045 / ISBN:https://doi.org/10.48550/arXiv.2402.00045 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Generation
3 Detection
4 Tools
5 Discussion
6 Conclusion
References
Authors' bios

Responsible developments and networking research: a reflection beyond a paper ethical statement / 2402.00442 / ISBN:https://doi.org/10.48550/arXiv.2402.00442 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Networking research today
3 Beyond technical dimensions
4 Sense of engagement and responsibility
5 Possible next steps
6 Concluding remarks
References
A Surveyed research group webpages

Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines / 2402.01659 / ISBN:https://doi.org/10.48550/arXiv.2402.01659 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related literature
3. Research study
4. Findings
5. Discussion
6. Conclusions, limitations, and future work
References

Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik's Cube / 2402.01760 / ISBN:https://doi.org/10.48550/arXiv.2402.01760 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Literature review
3. Methodology
4. Discussion
5. Conclusion
References
A. Macro Action for Solving the White Cross
B. An Example Dialog With Sentiment Analysis
C. ROSE: Tool and Data ResOurces to Explore the Instability of SEntiment Analysis Systems
D. CausalRating: A Tool To Rate Sentiments Analysis Systems for Bias

Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology / 2402.01762 / ISBN:https://doi.org/10.48550/arXiv.2402.01762 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Establishing the novel aspect of AI as a crossover technology
3 Moral and ethical obligations when developing crossover AI technology
4 Recommendations to address threats posed by crossover AI technology
5 Conclusion
References

(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice / 2402.01864 / ISBN:https://doi.org/10.48550/arXiv.2402.01864 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related work and our approach
3 Methods: case-based expert deliberation
4 Results
5 Discussion
6 Conclusion
References
A Provided AI response strategies and examples
B Workshop participant information
C Linear regression of participants' AI usage and desired responses

POLARIS: A framework to guide the development of Trustworthy AI systems / 2402.05340 / ISBN:https://doi.org/10.48550/arXiv.2402.05340 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 State of the practice
4 The POLARIS framework
5 POLARIS framework application
6 Limitations
7 Conclusion
Acknowledgments
References

A Framework for Assessing Proportionate Intervention with Face Recognition Systems in Real-Life Scenarios / 2402.05731 / ISBN:https://doi.org/10.48550/arXiv.2402.05731 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Background
3. Intervention models from other fields
4. Proposed framework
5. The framework in practice
6. Conclusions and future work
References

Ethics in AI through the Practitioner's View: A Grounded Theory Literature Review / 2206.09514 / ISBN:https://doi.org/10.48550/arXiv.2206.09514 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Review Methodology
4 Challenges, Threats and Limitations
5 Findings
6 Discussion and Recommendations
7 Methodological Lessons Learned
8 Conclusion
A List of Included Studies
C Glossary of Terms
Data Availability Statement
References
Authors

Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist / 2311.02107 / ISBN:https://doi.org/10.48550/arXiv.2311.02107 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Methods
Results
Discussion
Conclusion
Author contribution
Declaration of interests
Acknowledgement
Data sharing
Reference
Appendix

How do machines learn? Evaluating the AIcon2abs method / 2401.07386 / ISBN:https://doi.org/10.48550/arXiv.2401.07386 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Research methodology and text structure
3. AIcon2abs Instructional Unit
4. Results
5. Conclusion
Conflict of interest statement
References

I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench / 2401.17882 / ISBN:https://doi.org/10.48550/arXiv.2401.17882 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Awareness in LLMs
4 Awareness Dataset: AWAREEVAL
5 Experiments
6 Conclusion
Limitation
Ethical Statement
References
A AWAREEVAL Dataset Details
B Experimental Settings & Results

Mapping the Ethics of Generative AI: A Comprehensive Scoping Review / 2402.08323 / ISBN:https://doi.org/10.48550/arXiv.2402.08323 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Methods
3 Results
4 Discussion
5 Limitations
6 Conclusion
References
Appendix A
Appendix B
Appendix C

Taking Training Seriously: Human Guidance and Management-Based Regulation of Artificial Intelligence / 2402.08466 / ISBN:https://doi.org/10.48550/arXiv.2402.08466 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Emerging Management-based AI Regulation
3 Management-based Regulation and Human-Guided Training
4 Techniques of Human-Guided Training
5 Advantages of Human-Guided Training
6 Limitations
7 Conclusion
References

User Modeling and User Profiling: A Comprehensive Survey / 2402.09660 / ISBN:https://doi.org/10.48550/arXiv.2402.09660 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Analysis of the Terminology
3 Paradigm Shifts and New Trends
4 Current Taxonomy
5 Discussion and Future Research Directions
References

Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence / 2402.09880 / ISBN:https://doi.org/10.48550/arXiv.2402.09880 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background and Related Work
III. Unified Evaluation Framework For LLM Benchmarks
IV. Technological Aspects
V. Processual Elements
VI. Human Dynamics
VII. Discussions
VIII. Conclusion
References
Authors
Appendix A Examples of Benchmark Inadequacies in Technological Aspects
Appendix B Examples of Benchmark Inadequacies in Processual Elements
Appendix C Examples of Benchmark Inadequacies in Human Dynamics

Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implications / 2402.12216 / ISBN:https://doi.org/10.48550/arXiv.2402.12216 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 The AIGC Copyright Dilemma: A What-if Analysis
4 Case Study Under the Copyleft
5 Public Perception: A Survey Method
6 Implications and Recommendations
7 Conclusion
8 Ethical Statement
References

Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation / 2402.12590 / ISBN:https://doi.org/10.48550/arXiv.2402.12590 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Emergence of Free-Formed AI Collectives
3. Enhanced Performance of Free-Formed AI Collectives
4. Robustness of Free-Formed AI Collectives Against Risks
5. Open Challenges for Free-Formed AI Collectives
6. Conclusion
Impact Statements
References
A. Cocktail Simulation
B. Sentence Making Simulation
C. Public Good Simulation

What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents / 2402.13184 / ISBN:https://doi.org/10.48550/arXiv.2402.13184 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 CosmoAgent Simulation Setting
4 CosmoAgent Architecture
5 Evaluation
6 Experimental Design
7 Results
8 Conclusion
A CosmoAgent Prompt
B Secretary Agent Prompt
References

The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review / 2402.13635 / ISBN:https://doi.org/10.48550/arXiv.2402.13635 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Results
METRIC-framework for medical training data
Discussion
Methods
References

The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success / 2402.14728 / ISBN:https://doi.org/10.48550/arXiv.2402.14728 / Published by ArXiv / on (web) Publishing site

Abstract
1 The increasing importance of AI
2 The EU AI Act
3 There is no reliable AI regulation without a sound theory of human-AI interaction
4 There is no trustworthy AI without HCI
5 There is no community without common language and communication
6 Conclusion: Navigating the future of AI and HCI within the EU AI Act framework
References

Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education / 2402.15027 / ISBN:https://doi.org/10.48550/arXiv.2402.15027 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Materials and Methods
4 Analysis
5 Results
6 Discussion
References
Appendix 1 Scenarios
Appendix 2 Modified psychometric scales

Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms / 2402.17690 / ISBN:https://doi.org/10.48550/arXiv.2402.17690 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. The AI-Powered Development Life-Cycle in Autonomous Vehicles
III. Ethical Considerations and Bias in AI-Driven Software Development for Autonomous Vehicles
IV. AI’S Role in the Emerging Trend of Internet of Things (IOT) Ecosystem for Autonomous Vehicles
V. Review of Existing Research and Use Cases
VI. AI and Learning Algorithms Statistics for Autonomous Vehicles
VII. Conclusion
References
Authors

Envisioning the Applications and Implications of Generative AI for News Media / 2402.18835 / ISBN:https://doi.org/10.48550/arXiv.2402.18835 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 The Suitability of Generative AI for Newsroom Tasks
3 Conclusion
References

FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics / 2402.19071 / ISBN:https://doi.org/10.48550/arXiv.2402.19071 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Background
3. Methods
4. Results
5. Discussion
6. Conclusion
Declaration of Conflicting Interest
Funding
References

Guidelines for Integrating Value Sensitive Design in Responsible AI Toolkits / 2403.00145 / ISBN:https://doi.org/10.48550/arXiv.2403.00145 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 Methodology
4 Results
5 Discussion
6 Conclusion
References
A Survey Questions
B Toolkits Considered for Inclusion

Implications of Regulations on the Use of AI and Generative AI for Human-Centered Responsible Artificial Intelligence / 2403.00148 / ISBN:https://doi.org/10.48550/arXiv.2403.00148 / Published by ArXiv / on (web) Publishing site

Abstract
1 Motivation & Background
2 HCR-AI SIG at CHI 2023
3 Proposal & SIG’S Goal at CHI 2024
4 Expected Ooutcomes & Next Steps
References

Updating the Minimum Information about CLinical Artificial Intelligence (MI-CLAIM) checklist for generative modeling research / 2403.02558 / ISBN:https://doi.org/10.48550/arXiv.2403.02558 / Published by ArXiv / on (web) Publishing site

Abstract
Part 1. Study design
Part 1A. Study design for generative modeling
Part 1B. Best practices for cohort selection
Part 1C. Bias, privacy, and harm assessments
Part 2. A new train-test split for prompt development and few-shot learning
Part 3. Updates to baseline selection
Part 4. Model evaluation
Part 4A. Automated model evaluation
Part 4B. Human model evaluation
Part 5. Interpretability of generative models
Part 6. End-to-end pipeline replication
Conclusions
Table 1. Updated MI-CLAIM checklist for generative AI clinical studies.
References

Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipeline / 2403.03265 / ISBN:https://doi.org/10.48550/arXiv.2403.03265 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction & Motivation
II. Background & Literature Review
III. The AI-Enhanced CTI Processing Pipeline
IV. Challenges and Considerations
V. Conclusions & Future Research
References
Authors

A Survey on Human-AI Teaming with Large Pre-Trained Models / 2403.04931 / ISBN:https://doi.org/10.48550/arXiv.2403.04931 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 AI Model Improvements with Human-AI Teaming
3 Effective Human-AI Joint Systems
4 Safe, Secure and Trustworthy AI
5 Applications
6 Conclusion
References

Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines / 2312.05235 / ISBN:https://doi.org/10.48550/arXiv.2312.05235 / Published by ArXiv / on (web) Publishing site

References

Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metrics / 2311.05227 / ISBN:https://doi.org/10.48550/arXiv.2311.05227 / Published by ArXiv / on (web) Publishing site

References

Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance / 2206.11922 / ISBN:https://doi.org/10.48550/arXiv.2206.11922 / Published by ArXiv / on (web) Publishing site

References

How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities / 2311.09447 / ISBN:https://doi.org/10.48550/arXiv.2311.09447 / Published by ArXiv / on (web) Publishing site

B Baseline Setup
C Prompt Templates
D More Results

Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review / 2401.01519 / ISBN:https://doi.org/10.48550/arXiv.2401.01519 / Published by ArXiv / on (web) Publishing site

References

AGI Artificial General Intelligence for Education / 2304.12479 / ISBN:https://doi.org/10.48550/arXiv.2304.12479 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. What is AGI
3. The Potentials of AGI in Transforming Future Education
4. Ethical Issues and Concerns
5. Discussion
6. Conclusion
Declaration of generative AI and AI-assisted technologies in the writing process
References

Moral Judgments in Narratives on Reddit Investigating Moral Sparks via Social Commonsense and Linguistic Signals / 2310.19268 / ISBN:https://doi.org/10.48550/arXiv.2310.19268 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related Work
3. Data
4. Methods
5. Results
6. Discussion and Conclusion
References

Responsible Artificial Intelligence: A Structured Literature Review / 2403.06910 / ISBN:https://doi.org/10.48550/arXiv.2403.06910 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Research Methodology
3. Analysis
4. Discussion
5. Research Limitations
6. Conclusion
References

Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policies / 2403.08115 / ISBN:https://doi.org/10.48550/arXiv.2403.08115 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related Work
III. Problem Statement
IV. Informational Fairness
V. Representational Fairness
VI. Ethics and Morality
VII. Use Cases and Applications
VIII. Conclusion
References
Appendix

Towards a Privacy and Security-Aware Framework for Ethical AI: Guiding the Development and Assessment of AI Systems / 2403.08624 / ISBN:https://doi.org/10.48550/arXiv.2403.08624 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Theoretical Background
3 Research Methodology
4 Results of the Systematic Literature Review
5 Towards Privacy- and Security-Aware Framework for Ethical AI
6 Discussion and Limitations
7 Conclusion
References

Review of Generative AI Methods in Cybersecurity / 2403.08701 / ISBN:https://doi.org/10.48550/arXiv.2403.08701 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Attacking GenAI
3 Cyber Offense
4 Cyber Defence
5 Implications of Generative AI in Social, Legal, and Ethical Domains
6 Discussion
7 Conclusion
References
Appendix A GPT3.5 and GPT4 OCO-scripting

Evaluation Ethics of LLMs in Legal Domain / 2403.11152 / ISBN:https://doi.org/10.48550/arXiv.2403.11152 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Method
4 Experiment
5 Conclusion & Future Work
6 Limitations
__ The present study possesses certain limitations, specifically including the following: • Sole reliance on Chinese datasets without validation of feasibility on other languages. • Exclusive use of legal cases from the PRC, without addressing applicability in other legal systems. • The evaluation aspects may not be comprehensive, given the vast scope of legal ethics, with only a partial coverage attempted. • There is potential for expanding the number of LLM evaluated.
7 Ethic Impact
8 Broad Impact
References

Trust in AI: Progress, Challenges, and Future Directions / 2403.14680 / ISBN:https://doi.org/10.48550/arXiv.2403.14680 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Methodology
3. Findings
4. Discussion
5. Concluding Remarks and Future Directions
Conflict of interest
Acknowledgement
Reference

AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps / 2403.14681 / ISBN:https://doi.org/10.48550/arXiv.2403.14681 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Definitions
Method
Results
AI Ethics Development Phases Based on Keyword Analysis
Key AI Ethics Issues
Key Gaps
Limitations and Conclusion
Competing Interests
Funding
References
Authors bios

Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation / 2403.14706 / ISBN:https://doi.org/10.48550/arXiv.2403.14706 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Methodology
Data
Results
Conclusion
Web Appendix A: Analysis of the Disinformation Manipulations

The Journey to Trustworthy AI- Part 1 Pursuit of Pragmatic Frameworks / 2403.15457 / ISBN:https://doi.org/10.48550/arXiv.2403.15457 / Published by ArXiv / on (web) Publishing site

Abstract
1 Context
2 Trustworthy AI Too Many Definitions or Lack Thereof?
3 Complexities and Challenges
4 AI Regulation: Current Global Landscape
5 Risk
6 Bias and Fairness
7 Explainable AI as an Enabler of Trustworthy AI
8 Implementation Framework
9 A Few Suggestions for a Viable Path Forward
10 Summary and Next Steps
11 About the Authors
A Appendix
References

Analyzing Potential Solutions Involving Regulation to Escape Some of AI's Ethical Concerns / 2403.15507 / ISBN:https://doi.org/10.48550/arXiv.2403.15507 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Various AI Ethical Concerns
A Possible Solution to These Concerns With Business Self-Regulation
Feasibility of Business Self-Regulation
A Possible Solution to These Concerns With Government Regulation
Feasibility of Government Regulation
Conclusion
References

The Pursuit of Fairness in Artificial Intelligence Models A Survey / 2403.17333 / ISBN:https://doi.org/10.48550/arXiv.2403.17333 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Survey
3 Conceptualizing Fairness and Bias in ML
4 Practical cases of unfairness in real-world setting
5 Ways to mitigate bias and promote Fairness
6 How Users can be affected by unfair ML Systems
7 Challenges and Limitations
8 Conclusion
References

Domain-Specific Evaluation Strategies for AI in Journalism / 2403.17911 / ISBN:https://doi.org/10.48550/arXiv.2403.17911 / Published by ArXiv / on (web) Publishing site

1 Motivation
2 Existing AI Evaluation Approaches
3 Blueprints for AI Evaluation in Journalism
4 Future Directions and Conclusion
References

Power and Play Investigating License to Critique in Teams AI Ethics Discussions / 2403.19049 / ISBN:https://doi.org/10.48550/arXiv.2403.19049 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction and Related Work
2 Methods
3 RQ1: What Factors Influence Members’ “Licens to Critique” when Discussing AI Ethics with their Team?
4 RQ2: How Do AI Ethics Discussions Unfold while Playing a Game Oriented toward Speculative Critique?
5 Discussion
6 Conclusion
References

Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness / 2403.20089 / ISBN:https://doi.org/10.48550/arXiv.2403.20089 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Non-discrimination law vs. algorithmic fairness
3 Implications of the AI Act
4 Practical challenges for compliance
5 Conclusion
References
A Appendix

AI Act and Large Language Models (LLMs): When critical issues and privacy impact require human and ethical oversight / 2404.00600 / ISBN:https://doi.org/10.48550/arXiv.2404.00600 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. The implementation of the AI Act
3. The definition of artificial intelligence systems
4. AI Act and high-risk systems
5. Human Oversight
6. Large Language Models (LLMs) - Introduction
7. Artificial intelligence Liability
8. Conclusions
9. References

Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey / 2404.00990 / ISBN:https://doi.org/10.48550/arXiv.2404.00990 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Applications of Large Language Models in Legal Tasks
3 Fine-Tuned Large Language Models in Various Countries and Regions
4 Legal Problems of Large Languge Models
5 Data Resources for Large Language Models in Law
6 Conclusion and Future Directions
References

A Review of Multi-Modal Large Language and Vision Models / 2404.01322 / ISBN:https://doi.org/10.48550/arXiv.2404.01322 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 What is a Language Model?
3 Proprietary vs. Open Source LLMs
4 Specific Large Language Models
5 Vision Models and Multi-Modal Large Language Models
6 Model Tuning
7 Model Evaluation and Benchmarking
8 Conclusions
References

Balancing Progress and Responsibility: A Synthesis of Sustainability Trade-Offs of AI-Based Systems / 2404.03995 / ISBN:https://doi.org/10.48550/arXiv.2404.03995 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background and Related Work
III. Study Design
IV. Results
V. Discussion
VI. Threats to Validity
VII. Conclusion
Acknowledgment
References

Designing for Human-Agent Alignment: Understanding what humans want from their agents / 2404.04289 / ISBN:https://doi.org/10.1145/3613905.3650948 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Method
4 Findings
5 Discussion
6 Limitations
7 Conclusion
References

Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage / 2404.06077 / ISBN:https://doi.org/10.48550/arXiv.2404.06077 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Preliminaries
III. Proposed Design: IBIS
IV. Detailed Construction
V. Implementation on DAML
VI. Evaluation
VII. Conclusion
References

Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of Generative Agents / 2404.06750 / ISBN:https://arxiv.org/abs/2404.06750 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
A Primer
Polarised Responses
Rebooting Machine Ethics
Generative Agents in Society
Conclusion
References

Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation / 2403.14706 / ISBN:https://doi.org/10.48550/arXiv.2403.14706 / Published by ArXiv / on (web) Publishing site

Bibliography

The Pursuit of Fairness in Artificial Intelligence Models A Survey / 2403.17333 / ISBN:https://doi.org/10.48550/arXiv.2403.17333 / Published by ArXiv / on (web) Publishing site

A Appendices